Automated driving: The role of forecasts and uncertainty—A control perspective. (July 2015)

• Record Type: Journal Article
• Authors: Carvalho, Ashwin; Lefévre, Stéphanie; Schildbach, Georg; Kong, Jason; Borrelli, Francesco
• Is Part Of: European journal of control. Volume 24(2015:Jul.)
• Page Start: 14
• Page End: 32
• Publication Date: 2015-07
• Subjects: Automated driving -- Autonomous cars -- Model predictive control -- Adaptive learning; Control theory -- Periodicals; Automatic control -- Periodicals; Automatic control -- Mathematics -- Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.ejcon.2015.04.007
• Languages: English
• ISSNs: 0947-3580

Abstract

Abstract: Driving requires forecasts. Forecasted movements of objects in the driving scene are uncertain. Inevitably, decision and control algorithms for autonomous driving need to cope with such uncertain forecasts. In assisted driving, the uncertainty in the human/vehicle interaction further increases the complexity of the control design task. Our research over the past ten years has focused on control design methods which systematically handle uncertain forecasts for autonomous and semi-autonomous vehicles. This paper presents an overview of our findings and discusses relevant aspects of our recent results.